Communication between agents is a key capability necessary for effective cooperation in multi-agent systems.To communicate successfully, a speaker and a listener must share a common language to have a shared understanding of the symbols being used [1]}. Developing algorithms to induce such a common language (i.e. grounding communication) for emergent communication has been a difficult open problem. Existing works have attempted to address this challenge based on three main directions, namely centralized learning, differentiable communication, and supervised learning. To begin with, Centralized learning approaches, like [2]} and [3]}, share models among agents which implicitly ground communication to a common language to a certain extent. Differentiable communication approaches including [2]} and [5]} offer another grounding feedback by allowing gradients to flow through across agents. Lastly, supervised learning like [6]} leverages ground-truth information to adjust messages to obtain greater task rewards.
